The Boston Children’s Hospital Artificial Intelligence and Machine Learning working group gives our clinicians and investigators a forum for sharing knowledge and collaborating across the many facets of artificial intelligence and machine learning.

Core objectives:

  • create a forum for Boston Children’s Hospital investigators to find like-minded collaborators
  • foster an environment of knowledge exchange
  • collaborate on funding options to improve infrastructure
  • create a unified body for industry discussions

Focus areas:

  • clinical decision making
  • image processing and interpretation
  • hospital administrative functions and capacity planning
  • basic methods
  • life sciences and drug development
  • omics research and omics-informed medicine

Participating programs and sponsors include:

We host:

  • quarterly workgroup meetings
  • seminars
  • journal clubs

Please send an email to register your interest in joining.

Upcoming Lectures

For Bostons Children's Hospital clinicians and researchers, please email for a list of upcoming events.

Past Lectures

BCH AI and Machine Learning Journal Club

Speaker: Guergana Savova, PhD, Associate Professor of Pediatrics, Computational Health Informatics Program at Boston Children's Hospital

Date: December 8, 2020 at 4:45PM - 5:30PM

Dr. Savova led a discussion of tasks and applications of clinical Natural Language Processing (NLP) in medicine, such as: The landscape of neural approaches and clinical NLP (Wu et al, 2019; Data challenges in clinical NLP (de-identified data, usability and challenges) Some tasks and applications Information extraction for cancer surveillance (DeepPhe-CR) (Savova et al, 2017; Treatment information extraction (Bitterman et al, 2020; Lin et al, 2020 What is trending.

BCH AI and Machine Learning Journal Club

Speaker: Danielle Rasooly, PhD, Postdoctoral Fellow, Computational Health Informatics Program at Boston Children's Hospital

Date: November 10, 2020 at 4:45PM - 5:30PM

Dr. Rasooly led a discussion of the following paper about Google/DeepMind's AI system for breast cancer screening: McKinney et al. International evaluation of an AI system for breast cancer screening. Nature2020. as well as the following paper AI transparency/reproducibility: Haibe-Kains et al. Transparency and reproducibility in artificial intelligence. Nature 2020. ​The two papers are accessible as pdfs here.

The Age of Predictive Medicine

Speaker: Ben Reis, PhD, Faculty, Computational Health Informatics Program (CHIP); Director, Predictive Medicine Group, Computational Health Informatics Program (CHIP) Assistant Professor of Pediatrics, Harvard Medical School at Boston Children's Hospital

Date: October 16, 2020 at 09:30AM - 10:30AM

Dr. Ben Reis discussed recent developments in machine learning approaches to some of the grandest challenges of human health, including pandemic prediction, suicide prevention, bioterrorism detection, and drug safety prediction. The focus was on understanding both the methodological challenges involved and the ramifications of generating actionable predictions in these critical areas. The talk concluded by formulating a set of central challenges and opportunities facing the field of Predictive Medicine.

BCH AI and Machine Learning Working Group Lightning Talks

Date: September 9, 2020 at 09:30AM - 10:30AM

The BCH AI and Machine Learning Working Group held our first Lightning Talks session, where multiple investigators gave brief overviews of numerous Machine Learning applications at Boston Children’s Hospital to foster clinical and machine learning collaborations across the hospital.

A Gold Mine of Potential: Predictive Analytics Using Boston Children’s Hospital’s “Children’s 360” Data Warehouse

Speaker: Jonathan Bickel, MD, MS; Ronald Wilkinson, MA, MS, CBIP; Ashley Doherty, MS, at

Date: August 14, 2020 at 09:30AM - 10:30AM

Boston Children’s Hospital data warehouse integrates 15 years of extensive clinical and administrative data sources and more years of selected data sources. While the contents are used extensively for daily operational reporting, the potential for extensive retrospective and predictive analytics is largely untapped. Jonathan Bickel, Ashley Doherty, and Ron Wilkinson will show something of the breadth of data available in the EDW, discuss how predictive modeling tools can access the data, discuss ideas for predictive modeling applications that they think would be valuable, and explain the conditions on which access to the data can be granted.

AI in 3D Medical Images: Concepts, Milestones, and Opportunities

Speaker: Yangming Ou, PhD, Assistant Professor of Radiology; Affiliate Faculty, Computational Health Informatics Program; Faculty, Fetal-Neonatal Neuroimaging Data Science Center at Boston Children's Hospital

Date: July 17, 2020 at 09:30AM - 10:30AM

Dr. Yangming Ou briefly reviewed some major concepts and milestones of AI in medical images. The focus of Dr. Ou’s talk was on 3D medical images, for AI’s application in disease diagnosis, outcome prediction, early screening, neuroscience, and others. Dr. Ou then discussed some major challenges and potential opportunities, including further improving accuracy in detecting small diffuse lesions, and facilitating AI in small sample sizes.

BCH AI and Machine Learning Journal Club

Speaker: Tim Miller, PhD, Assistant Professor of Pediatrics, Computational Health Informatics Program at Boston Children's Hospital

Date: June 30, 2020 at 4:45PM - 5:30PM

Dr. Timothy Miller discussed articles that he recently published on natural language processing of computerized text. 1. Dligach D, Majid A, Miller T. Toward a Clinical Text Encoder: Pretraining for Clinical Natural Language Processing With Applications to Substance Misuse. SSRN. 2020. 2. Miller T, Avillach P, Mandl K. Experiences Implementing Scalable, Containerized, Cloud-based NLP for Extracting Biobank Participant Phenotypes at Scale. SSRN. 2020.

BCH AI and Machine Learning Journal Club

Speaker: Arjun (Raj) Manrai, PhD, Faculty, Computational Health Informatics Program (CHIP); Director, Laboratory for Probabilistic Medical Reasoning; Assistant Professor, Harvard Medical School at Boston Children's Hospital

Date: May 8, 2020 at 09:30AM - 10:30AM

Blood laboratory measures such as glucose and hemoglobin are the basis for much of clinical decision making, yet baseline variation for many laboratory measures remains incompletely characterized across age, gender, and race groups. I will introduce foundational techniques from machine learning and statistical genetics and show how they can be applied to systematically unpack variation in blood laboratory data across population groups. These analyses reveal widespread demographic structure in blood laboratory data.


Kiang MV, Santillana M, Chen JT, Onnela JP, Krieger N, Engø-Monsen K, Ekapirat N, Areechokchai D, Prempree P, Maude RJ, Buckee CO. Incorporating human mobility data improves forecasts of Dengue fever in Thailand. Scientific reports 2021.

Rubinstein YR, Robinson PN, Gahl WA, Avillach P, Baynam G, Cederroth H, Goodwin RM, Groft SC, Hansson MG, Harris NL, Huser V, Mascalzoni D, McMurry JA, Might M, Nellaker C, Mons B, Paltoo DN, Pevsner J, Posada M, Rockett-Frase AP, Roos M, Rubinstein TB, Taruscio D, van Enckevort E, Haendel MA. The case for open science: rare diseases. JAMIA open 2020.

Ferenczi S, Solymosi N, Horváth I, Szeőcs N, Grózer Z, Kuti D, Juhász B, Winkler Z, Pankotai T, Sükösd F, Stágel A, Paholcsek M, Dóra D, Nagy N, Kovács KJ, Zanoni I, Szallasi Z. Efficient treatment of a preclinical inflammatory bowel disease model with engineered bacteria. Molecular therapy. Methods & clinical development 2021.

Liu D, Olson KL, Manzi SF, Mandl KD. Patients dispensed medications with actionable pharmacogenomic biomarkers: rates and characteristics. Genetics in medicine : official journal of the American College of Medical Genetics 2021.

Diao JA, Inker LA, Levey AS, Tighiouart H, Powe NR, Manrai AK. In Search of a Better Equation - Performance and Equity in Estimates of Kidney Function. The New England journal of medicine 2021.